From Analytics Engineer
to AI Engineer
You already build production data pipelines and models.
Now learn to add the AI layer on top of your strong foundation.
AI Native Engineer Community Access
You Model the Data.
Others Build What Uses It.
AI engineers earn 30-50% more while building on top of your data models.
Your dbt and SQL expertise is production ready, but ML feels like a separate world.
You are unsure which AI skills to prioritize given your analytics engineering background.
Your Data Modeling Skills Are the Foundation.
The AI Career Accelerator
Analytics engineers have rare advantages in AI: you understand data quality, transformations, testing, and documentation. You already think in production terms. Learn to add embeddings, vector stores, and LLM integration to your existing dbt and SQL expertise.
Leverage Your Stack
Your dbt, SQL, and orchestration skills transfer directly
Add the AI Layer
Embeddings, vector databases, and LLM integration
Position Your Experience
Frame analytics engineering as AI infrastructure expertise
Meet Your Mentor
When I started in tech, I was based in the Netherlands with no connections and only thousands of video game hours under my belt. Not exactly the ideal starting point.
My first tech job was software tester. One of the most junior roles you can start with. I was just happy someone took a chance on me.
I kept learning. Kept pivoting. But what actually accelerated my career wasn't more certifications or more code. It was learning to solve problems that matter and proving beyond a doubt that what I built solved real problems. That's the skill that stays future-proof, even with AI.
I've since worked remotely for international software companies throughout my career. Proof that the high-paid remote path is possible for anyone with the right skills and motivation. In the end, I went from a $500/month internship to 6 figures as a Senior AI Engineer at GitHub.
Now I teach over 22,000 engineers on YouTube. Becoming an AI-Native Engineer is a system I lived through and offer to you today.
Real Results
Vittor
AI Engineer
Landed his first AI Engineering role in 3 months
"The coaching played a huge part in my success. I focused on AI fundamentals, the certification path, and soft skills like professional writing. Having access to expert guidance gave me confidence during interviews and helped me feel I was on the right path.
I built my own platform (simple but functional) and deployed it on AWS. I used it in my portfolio and showcased it during interviews. The way complex topics were explained, especially the restaurant analogy for AI systems, really stuck with me. Focusing on doing the basics well was absolutely essential."
What You Will Get
Personalized Roadmap & Career Strategy
A custom plan tailored to your background, goals, and timeline. No generic advice.
Weekly 1:1 Coaching Calls
Direct access to Zen for guidance, project feedback, and answers to your questions.
Portfolio-Ready AI Projects
Build production-grade AI applications to showcase to employers. Work that gets you hired.
Interview Prep & Mock Interviews
Practice technical and behavioral interviews. Learn what hiring managers look for.
Resume & LinkedIn Optimization
Transform your online presence to attract recruiters. Stand out from other applicants.
Community Career Support
Join the AI Native Engineer community. Not seeing results yet? You stay and keep going. We're with you through the ups and downs.
Analytics Engineers Have Production Data Instincts
Every month you delay can cost you thousands in lost earning potential. While you're watching tutorials, others are landing $120K+ AI Engineering roles.
I can only work with a limited number of 1:1 clients at a time to ensure you get the personalized attention you deserve.
Frequently Asked Questions
Why are analytics engineers well positioned for AI roles?
Analytics engineers bring production data skills that AI teams desperately need. You already understand data quality, testing, documentation, and transformation pipelines. Your dbt experience means you think in modular, tested, version controlled code. Many ML engineers lack these fundamentals and struggle with data quality issues you solve daily. Your gap is narrower than pure data analysts: add embeddings, vector databases, and model serving to complete your AI toolkit.
What should analytics engineers learn for AI engineering?
Build on your strengths. Priority order: (1) Vector databases and embeddings, a natural extension of your data modeling skills. (2) Feature engineering pipelines, similar to your dbt transformations. (3) RAG systems, combining your SQL expertise with LLM capabilities. (4) Model serving basics, deploying models as APIs. Your orchestration experience with Airflow or Dagster transfers to ML pipelines. Focus on the data infrastructure side of AI systems where your skills shine.
Do I need prior AI experience?
Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.
How much time do I need to commit?
Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.
How is this different from online courses?
Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.
What if I don't land interviews in 90 days?
You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.
What's the investment for 1:1 coaching?
Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.
Can I do this while working full-time?
Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.
Ready to Land Your AI Role?
Stop watching others succeed. Start building your AI career today.
30-minute strategy call • Limited spots available